Unverified50% confidenceFactExact time
Jev-style decision models need to decode only 1-2 tokens at inference time, so latency and VRAM usage are far lower than free generation.
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50%
Confidence
Long-term
Relevance
10/15/2026
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UnverifiedJev attaches a confidence score of 0-1 to each answer, enabling downstream systems to write explicit threshold logic (such as handing off to a human when confidence is below a threshold).71% similarUnverifiedJev's core value lies in unlocking batch decision scenarios that have never been done before because they were too slow or too expensive, rather than replacing generative models.71% similarUnverifiedThe output space of a decision model is fixed to a finite set of options, allowing all candidates to be scored in parallel rather than decoded sequentially, which greatly reduces latency.70% similarUnverified大模型每个数字都是逐 token 生成的，而非逐位计算，导致其算术能力在小数、除法、多步混合运算上不稳定69% similarUnverifiedJev's speed and cost advantages stem from abandoning general-purpose text generation, performing parallel sampling over a constrained decision space, and bypassing the serial bottleneck of autoregressive decoding.68% similar
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